Artificial Intelligence on How Marketing Technology Works®

How to Evaluate an AI Vendor in 30 Minutes

Mon, 04 May 2026 15:46:00 -0400

"I mandated AI at my company. It almost backfired."

Sun, 03 May 2026 17:18:00 -0400

Why Agentic AI Projects Fail: The Decision Architecture Gap

Fri, 01 May 2026 12:00:00 +0000

Adobe’s CX Enterprise Coworker launched at Summit 2026 with a clear pitch: give it a business objective, and it assembles agents across your CDP, journey analytics, and content optimizer to build and execute the plan (1. Adobe, 2026). Salesforce introduced Agentforce Operations the same month, automating back-office workflows with over 30 blueprints for everything from invoice auditing to onboarding. The technology vendors have delivered on agentic AI. The question nobody’s answering: has anyone on the receiving end built the infrastructure to run it?

Your Vendor Calls It Agentic. Your Operating Model Doesn't Care.

Fri, 01 May 2026 12:00:00 +0000

Every major martech vendor now sells something labeled “agentic AI.” Eighty-eight percent of organizations are experimenting with it. But 81% of those organizations report no meaningful bottom-line gains (1. McKinsey, 2026).

That number should stop the conversation. It doesn’t. Boards want an “AI strategy.” Vendors pitch autonomy. CMOs worry about falling behind. The entire ecosystem is optimized to accelerate adoption, and nobody is asking why adoption isn’t producing results.

The answer has two fronts. On the supply side, vendors are dressing up legacy automation as agentic AI. On the demand side, organizations are deploying whatever they buy onto operating models that can’t support autonomous execution. The technology sits between a vendor credibility problem and a buyer readiness problem, and the gap between investment and outcome keeps getting wider.

AI Agent Governance: Why Centralized Approval Backfires

Thu, 30 Apr 2026 12:00:00 +0000

As AI agents multiply across marketing teams, most leaders reach for the same response: require every agent to be registered and approved before it runs. Centralized control. A governance committee. An approval queue.

That response feels responsible. It produces the opposite of its intended result.

When the official approval process takes a week and building an agent independently takes an afternoon, teams choose speed. A marketing ops manager who needs a campaign performance agent running before next week’s leadership meeting isn’t going to wait for a governance committee to meet. They’ll build it with their own API credentials, connect it to the data sources they have access to, and have it running by Thursday. The agent doesn’t appear in anyone’s inventory. Nobody tracks what it can access.

Operationalize Marketing, Not AI

Tue, 28 Apr 2026 12:00:00 +0000

The marketing industry is treating AI as the thing that needs to be deployed, integrated, governed, and scaled. It’s not. AI is a diagnostic instrument, and what it diagnosed is uncomfortable: marketing operations were never fully operationalized in the first place.

The evidence is broad and consistent. Six independent marketing communities, surveyed separately in 2026, converge on the same structural failures: bad data, unclear positioning, misaligned stakeholders, fragmented stacks, broken executive alignment (1. Gene De Libero, Cross-Community Research Synthesis, 2026). None of these problems are new. None of them are caused by AI. They’re the organizational debt that accumulates when marketing teams buy platforms instead of building the operational infrastructure to run them.

Enterprise AI That Learns: Marketing Leader Success Guide

Fri, 03 Apr 2026 16:03:00 -0400

Static AI is the expensive default

Marketing organizations face a pattern that repeats across industries: deploy an AI tool, get decent initial results, then watch performance flatline. Content systems produce solid output during testing but can’t learn which messages drive engagement. Email platforms send campaigns but forget which subject lines converted. Lead scoring tools run the same algorithms regardless of what the sales team reports back.

MIT’s NANDA initiative found that 95% of enterprise AI initiatives deliver zero measurable return despite $30-40 billion in aggregate spending (1. MIT NANDA, 2025). These tools fail because they lack three core capabilities: they can’t remember what happened, they can’t adjust based on results, and they don’t connect to existing tools in ways that let them learn. Static by design. Useful once. Then diminishing returns.